DarkArrow at Planetary Qualifier Oviedo

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Planetary Premier April 25, 2026 Record 4-2-0 Field 44

Rating after 1,514

+72

Rating before 1,442 RD 113

Rating after 1,514 RD 105

Effective multiplier 1.0×

Performance 1,869

Field strength Mean 1,545 · Median 1,519 · 44 rated

// rating math · glicko-2 phase replay

How this rating change was computed

Glicko-2 doesn’t reward record — it rewards surprise. Winning a match the system expected you to win is worth almost nothing. Losing one is expensive. Losing to someone rated below you costs the most. The Planetary tier multiplier (1.0×) amplifies every gain and every loss.

Shape D top-cut adjustments QF SF F Wins +0.12 +0.18 +0.25 Losses -0.18 -0.27 -0.35

Making cut at this Planetary event adds a flat +15 top-cut bonus on top of phase math. Making cut never costs you net rating — the made-cut floor pins the tournament delta to ≥ 0.

Start 1442 → Swiss 1514 +72.4

Top Cut Bracket Full bracket on the event page

Quarterfinals

JAEstwald 1

Diego-jr 2

Loira 2

Farreli 0

Evilcloud 0

miguividal 2

Fuegoscuro86 2

McLion34 1

Semifinals

Diego-jr 0

Loira 2

miguividal 2

Fuegoscuro86 1

Finals

Loira 2

miguividal 1

See full bracket on the event page →

Swiss · 6 matches 4-2 +72.4 1.0× +0.0 +72.4 1514

Total 4-2-0 +72.4 1514

Biggest upset Beat miguividal 1845 at 20% odds / surprise +0.8

Costliest loss Lost to JAEstwald 1558 at 40% odds / surprise -0.4 /

Matches (6)

R1 McLion34 Win 2-1 1.0× +20.0 Swiss

R2 SSB_Lier Loss 0-2 1.0× Swiss

R3 Ferrepower Win 2-1 1.0× +28.3 Swiss

Glicko-2 predicted only 33% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect. Their rating wasn't well-established (RD 156, true skill could span ±313).

R4 SSB_Bortx Win 2-0 1.0× +31.1 Swiss

Glicko-2 predicted only 28% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R5 miguividal Win 2-0 1.0× +34.3 Swiss

Glicko-2 predicted only 20% odds for you. Upset wins move the rating model strongly because the system learns a lot from results it didn't expect.

R6 JAEstwald Loss 1-2 1.0× Swiss